지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.12
- 수록면
- 717 - 727 (11page)
- DOI
- 10.5050/KSNVE.2024.34.6.717
이용수
초록· 키워드
오류제보하기
Recent trends in facility maintenance have focused on automating defect diagnosis using artificial intelligence(AI) technologies. One such method utilizing AI is rule-based diagnosis, which operates based on decisions made from expert knowledge and does not require large datasets. However, because it evaluates only the data measured at a single point, the scope of evaluation is limited and requires further development. This paper presents the development of a multi-point rule-based diagnostic system that evaluates the entire system by considering measurement results from multiple points. The performance of the proposed diagnostic method is validated by comparing the results of direct diagnoses with those obtained through rule-based diagnosis, using durability test data from a vertical pump. The durability test results were categorized into four conditions based on their progression, and the diagnostic outcomes were compared at each step.
목차
ABSTRACT
1. 서론
2. 규칙기반 진단 기술 강화
3. 실험
4. 적용 결과
5. 결론
References
참고문헌 (15)
참고문헌 신청
Kim, T. H., Kim, H. S., Kim, D. K., Kim, W. T. and Han, B. S., 2009, The Vibration Impact Assessment for Long-term Damage Mechanism of a Pump, Proceedings of the KSNVE Annual Autumn Conference, pp. 441~445.
Kim, S. H., Chung, J. H. and Baek, W. K., 2021, A Study on A Motor Noise Diagnosis Method using Voice Recognition and Machine Learning Techniques, Transactions of the Korean Society for Noise and Vibration Engineering, Vol. 31, No. 1, pp. 40~46.
Back, J., Kim, S.-W., Lee, S.-K. and Lee, C., 2020, Conditioning Monitoring in Chain Sprocket Drive Unit System based on Artificial Neural Network, Transactions of the Korean Society for Noise and Vibration Engineering, Vol. 30, No. 3, pp. 286~293.
Jung, J. H., Sun, K. H. and Kim, K., 2021, Fault Diagnosis Method for Excavator Hydraulic Axial Piston Pumps, Journal of Drive and Control, Vol. 18, No. 4, pp. 98~103.
Lim, Y.-H., Cho, H.-J. and Lee, H., 2023, Deep-learning-based Turbo Charger Vibration Analysis Utilizing Canny Edge Detection and Convolutional Neural Networks Technique, Transactions of the Korean Society for Noise and Vibration Engineering, Vol. 33, No. 5, pp. 539~548.
Kim, S. H., Chung, J. H. and Baek, W. K., 2021, A Study on A Motor Noise Diagnosis Method using Voice Recognition and Machine Learning Techniques, Transactions of the Korean Society for Noise and Vibration Engineering, Vol. 31, No. 1, pp. 40~46.
Back, J., Kim, S.-W., Lee, S.-K. and Lee, C., 2020, Conditioning Monitoring in Chain Sprocket Drive Unit System based on Artificial Neural Network, Transactions of the Korean Society for Noise and Vibration Engineering, Vol. 30, No. 3, pp. 286~293.
Jung, J. H., Sun, K. H. and Kim, K., 2021, Fault Diagnosis Method for Excavator Hydraulic Axial Piston Pumps, Journal of Drive and Control, Vol. 18, No. 4, pp. 98~103.
Lim, Y.-H., Cho, H.-J. and Lee, H., 2023, Deep-learning-based Turbo Charger Vibration Analysis Utilizing Canny Edge Detection and Convolutional Neural Networks Technique, Transactions of the Korean Society for Noise and Vibration Engineering, Vol. 33, No. 5, pp. 539~548.
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